Marta Maria Sesana

The Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-driven approach. This study conducted a nationwide analysis of over 40,000 school buildings. After incomplete or inconsistent records were filtered out, a refined subset was selected. Building forms were reconstructed by cross-referencing GIS data with multiple open data sources. Using supervised machine learning, the research identifies and classifies recurring morphological patterns to define a set of 3D school building archetypes. These archetypes are enriched with spatial configurations and physical characteristics aligned with national educational standards. The result is a macrotypological classification based on form, conceived as part of an operational tool to support policymakers, designers, and public administrations in selecting effective retrofit strategies. This contributes to the creation of large-scale national renovation strategies, as well as Renovation Roadmaps and Digital Building Logbooks in line with the Energy Performance of Buildings Directive (EPBD IV), specifically tailored to the Italian context. The novelty of this work lies in its unprecedented scale and the use of AI to enable fast, replicable assessments of retrofit potential, thereby supporting informed decisions in energy-efficient renovation planning.